Equivariant Neural Networks: 
Pose-equivariant networks are equivariant to input pose [1]}, [2]}, [3]}, [4]} by design.
Some of these methods use Spherical Harmonic functions [1]}, [4]}, [3]}, [8]}, or vector neurons [2]} to extract equivariant features.
Equivariance is closely related to canonicalization since a canonical pose is also equivariant.
Thus, previous methods have used pose-equivariant for canonicalization [10]}, [11]}, [12]}, [13]}.
However, these methods have thus far been limited to 3D point clouds, voxels, or meshes.
